Bibliographic record
Abstract
본 연구는 실험패러다임을 활용하여 정서조절전략의 종류와 선택방법이 부적정서 완화에 미치는 영향에 대해 검증해보고자 하였다. 연구도구로 정서조절과제, 즉 정서가가 있는 사진을 보고 다양한 지시사항에 따라 부적정서를 측정하는 방식의 과제를 활용하였다. 본 연구에서는 인지적 재평가와 회피 전략을 활용하여 본인이 혼자 전략을 선택하여 부적정서를 측정하는 조건과 타인이 선택해준 전략을 활용한 후 부적정서를 측정하는 조건을 살펴보았다. McGill 관계만족도 척도, CES-D10 단축형 우울 척도, STAI 특성불안검사, TAS-20K 감정표현불능증 척도를 사전에 온라인으로 실시하고 연구 참여 대상자를 선정, 총 36팀 72명의 대학생 참가자의 자료를 분석하였다. 먼저 정서조절전략의 종류에 따른 효과를 확인하기 위한 일원분산분석을 실시한 결과, 인지적 재평가 전략을 활용하였을 때 본인 선택 조건과 타인 선택 조건 모두에서 부적정서 점수가 낮아진 반면, 회피 전략에 대해서는 두 선택 조건 모두에서 부적정서 점수가 오히려 높게 나타났다. 또한 선택방법과 정서조절 여부에 관해 이원분산분석을 실시한 결과, 정서조절의 주효과와 선택방법과 정서조절의 상호작용 효과를 확인하였다. 본 연구는 새로운 도구인 정서조절과제와 정서가를 가진 사진을 활용하여 상담연구에 활용하고, 타인정서조절 개념의 활용과 또래상담연구에 추가적인 시사점을 제공하였다는 점에서 의미를 지닌다.\n\nThis study used experimental paradigm(Emotion Regulation Task) to examine the influence of emotion regulation strategy and selection types on relieving negative affect. IAPS(pictures with valence) was utilized to rate the negative affect following various instructions. Participants utilized cognitive reappraisal or distraction strategies, either by choosing and performing by oneself(intrapersonal regulation) or following the partner's choice(interpersonal regulation). Participants completed McGill Relationship Questionnaire, CES-D10, STAI, and TAS-20K scale as they signed up online for the experiment. Data of total 36 teams (72 university students) were analyzed. One-Way-ANOVA was conducted to see the influence of strategy types, resulting that when using cognitive reappraisal, the score of negative affect decreased in both selection condition(intrapersonal and interpersonal), while when using distraction, the score of negative affect increased. Two-Way-ANOVA was conducted and showed that there was a main effect in selection type and interaction effect in emotion regulation strategies and selection types. This study is significant in that it used scientific tools such as, Emotion Regulation Task and IAPS, and the concept of interpersonal emotion regulation and provided additional implications for peer-counseling research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".